T cells exhibit unexpectedly low discriminatory power and can respond to ultra-low affinity peptide-MHC ligands

  1. Johannes Pettmann
  2. Enas Abu-Shah
  3. Mikhail Kutuzov
  4. Daniel B. Wilson
  5. Michael L. Dustin
  6. Simon J. Davis
  7. P. Anton van der Merwe
  8. Omer Dushek

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Abstract

T cells use their T cell receptors (TCRs) to discriminate between peptide MHC (pMHC) ligands that bind with different affinities but precisely how different remains controversial. This is partly because the affinities of physiologically relevant interactions are often too weak to measure. Here, we introduce a surface plasmon resonance protocol to measure ultra-low TCR/pMHC affinities (K D ~ 1000 μ M). Using naïve, memory, and blasted human CD8 + T cells we find that their discrimination power is unexpectedly low, in that they require a large >100-fold decrease in affinity to abolish responses. Interestingly, the discrimination power reduces further when antigen is presented in isolation on artificial surfaces but can be partially restored by adding ligands to CD2 or LFA-1. We were able to fit the kinetic proof-reading model to our data, yielding the first estimates for both the time delay (2.8 s) and number of biochemical steps (2.67). The fractional number of steps suggest that one of the proof-reading steps is not easily reversible.

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  1. PREreview

    This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/4695222.

    Summary

    Pettman et al. present a commendable approach to a pressing enigma in T cell immunology: how does a T cell receptor (TCR) differentiate between its target and off-targets? The authors developed an SPR method to measure TCR affinity for off-target peptide-MHC complexes that interact with lower affinity than was previously detectable by existing SPR-based approaches . They paired SPR measurements with substantial in vitro work to correlate biophysical affinity with human T cell agonism. This was followed by application of the kinetic proof-reading (KP) model of TCR recognition to produce values for the number of fractional steps and time delay required for TCR target proofreading. The article is well-presented and for the most part, the article text and figures are understandable and the data is well visualized for easy interpretation. There are some major issues that require clarification, however. 

     

    Overall, this study advances the field of T cell immunology in three pertinent ways. 1. The authors present an SPR protocol to standardize TCR-pMHC measurements by using W6/32 binding as a reference. 2. The authors convincingly show that their SPR protocol is able to directly detect low affinity TCR-pMHC interactions that would otherwise not be detected, therefore the authors can fully gauge the discriminatory power of TCRs by measuring both high and low affinity interactions. 3. The SPR measurements conducted by the authors convincingly correlate with in vitro activation assays in naive, memory, and blastic human T cells. It is not so convincing that this data is only explained by the KP model, however. Upon publication, I feel others in the field of T cell immunology, especially TCR therapeutics, will find this article thought-provoking.

     

    Major Issues

    1. A completely irrelevant HLA-A0201-binding peptide for each TCR, such as CMV pp65 NLV or EBV LMP2 CLG, should be included in SPR and in vitro assays as it would provide a guiding threshold for TCR recognition that would enrich the authors' argument. As is, the graded responses seen for each peptide variant imply that TCRs are highly discriminatory with regards to differentiating variants of their target peptide, thereby contradicting the authors' main conclusion.
    2. The flow cytometry experiments require more detail. Fig. 2B: CD69 upregulation plots should be accompanied by at least one representative flow plot showing how CD69+ cells were gated. The authors should clarify the number of replicates for flow cytometry data used to measure CD69 %, number of events acquired per replicate, and whether each datapoint represents the mean of the replicates or another metric. In the methods, Flow cytometry section (line 356) should specify the incubation times and dilutions for antibody staining, and should provide details regarding events collected per sample and gating strategy. 
    3. The results are largely supported by the conclusions; however, an adequate discussion of where the occupancy model fails to model TCR sensitivity and discrimination is needed. Adhesion dynamics models have previously evaluated the coupled role of affinity and avidity with regards to integrin interactions, so it is not clear to me why the KP model is germane to the authors' study. For example, in Fig. 4: it is unclear what is the rationale for fitting the nonlinear KP model to the p_15 vs. K_D data when the prior figures use a linear fit for this data to a reasonable approximation. The authors should make it clear either in the text or in the figures why they now turn to the KP model to fit this data, beyond simply obtaining the descriptive parameters it produces. 
    4. The title should also be reconsidered as it only pertains to discriminatory power as defined by the authors, but is otherwise not supported by the data. That so many variants of the NYESO1 peptide-MHC bind the 1G4 TCR with high K_D yet are still recognized with measurable P_15 but in graded responses suggests that TCRs have extremely high discriminatory power. To this end, the above irrelevant A2 peptide control, as mentioned in specific comment 1, will provide a helpful threshold.

     

    Minor Issues

    1. The schematic in Fig. 1A should illustrate more clearly the role of W6/32 in amplifying the signal of low affinity interactions. Visually, it should be made clear that Fig 1A-C divides into left and right columns based on affinity.
    2. The spearman correlation or other fit parameter for p_15 vs. K_D plots should be added.
    3. The expression plasmids should be specified in the methods section. Soluble ECD production (lines 251-264) should include a description of how the gene was obtained, e.g. synthetic gBlock was ligated into expression plasmid, cDNA cloned from mRNA, etc, as well as a brief mention of the transfection technique.
    4. Line 293 should specify the format of nucleic acids for TCR alpha, beta, and CD3z.
    5. Lines 21-23 would benefit from the addition of DMF5 TCR characteristics as its crossreactivity has been probed in papers referenced by the authors.

     

    I would like to thank the authors for performing this work and making the preprint available to all.

     

     

  2. Version published to 10.1101/2020.11.14.382630v1 on bioRxiv